However, there is still a gap between research and practice, hindering the adoption of IML, with most current critics being methodological. For example, it was shown that several approaches are not robust or consistent in their explanation [1]}, [2]}, [3]} and point out untrue relationships in the data or model, e.g., rather reconstructing the input image or performing edge detection than estimating the feature importance [1]}. Further, untrue explanations are often accepted through the human confirmation bias, falsely rejecting true but less human appealing explanations [5]}. Evaluation methods lack a ground truth label and, as a proxy, use performance metrics such as accuracy to determine the importance of a feature to the model's prediction [6]}. This results in an evaluation of correlation rather than causal concepts [7]}. Further, the evaluation through removing attributed features can create out-of-distribution (OOD) or adversarial examples, which then evaluate more the robustness of the model than the importance of the features [8]}. While methodological criticism is important to the engineering of IML methods, the focus of our proposal lies on the formal process, which either adapts current methods to its needs or highlights where the repertoire has to be expanded.
